Federated learning (FL) is a promising distributed machine learning architecture that allows
participants to cooperatively train a global model without sharing local data. However, both …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

The potential of Federated Learning (FL) deployment increases rapidly as the number of
connected devices increases, the value of artificial intelligence is recognized and …

Federated learning has garnered significant attention and been widely explored in various
domains as a means to train models while preserving data privacy through local data …

Federated Learning (FL) has gained prominence as a machine learning framework
incorporating privacy-preserving mechanisms. However, challenges such as poisoning …

The Internet of Things (IoT) is revolutionizing numerous industrial applications by employing
smart devices in manufacturing and industrial processes. Industries based on IoT generate …

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

Privacy and data security have become the new hot topic for regulators in recent years. As a
result, Federated Learning (FL)(also called collaborative learning) has emerged as a new …

During the last few years, federated Learning (FL), an AI paradigm shift, has become an
active research field, coming to allow the collaborative training of machine/deep learning …

Federated learning (FL) has gained widespread popularity in recent years due to the fast
booming of advanced machine learning and artificial intelligence, along with emerging …